BrainSec: Automated Brain Tissue Segmentation Pipeline for Scalable Neuropathological Analysis

Zhengfeng Lai, Luca Cerny Oliveira, Runlin Guo, Wenda Xu, Zin Hu, Kelsey Mifflin, Charles DeCarli, Sen Ching Cheung, Chen Nee Chuah, Brittany N. Dugger

Research output: Contribution to journalArticlepeer-review


As neurodegenerative disease pathological hallmarks have been reported in both grey matter (GM) and white matter (WM) with different density distributions, automating the segmentation process of GM/WM would be extremely advantageous for aiding in neuropathologic deep phenotyping. Standard segmentation methods typically involve manual annotations, where a trained researcher traces the delineation of GM/WM in ultra-high-resolution Whole Slide Images (WSIs). This method can be time-consuming and subjective, preventing a scalable analysis on pathology images. This paper proposes an automated segmentation pipeline (BrainSec) combining a Convolutional Neural Network (CNN) module for segmenting GM/WM regions and a post-processing module to remove artifacts/residues of tissues. The final output generates XML annotations that can be visualized via Aperio ImageScope. First, we investigate two baseline models for medical image segmentation: FCN, and U-Net. Then we propose a patch-based approach, BrainSec, to classify the GM/WM/background regions. We demonstrate BrainSec is robust and has reliable performance by testing it on over 180 WSIs that incorporate numerous unique cases as well as distinct neuroanatomic brain regions. We also apply gradient-weighted class activation mapping (Grad-CAM) to interpret the segmentation masks and provide relevant explanations and insights. In addition, we have integrated BrainSec with an existing Amyloid-β pathology classification model into a unified framework (without incurring significant computation complexity) to identify pathologies, visualize their distributions, and quantify each type of pathologies in segmented GM/WM regions, respectively.

Original languageEnglish (US)
JournalIEEE Access
StateAccepted/In press - 2022
Externally publishedYes


  • Alzheimer’s disease and dementia
  • Annotations
  • Brain modeling
  • convolutional neural network
  • Convolutional neural networks
  • Diseases
  • Image resolution
  • Image segmentation
  • medical image analysis
  • Neuropathology
  • Pipelines

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)


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